1,127 research outputs found

    Optimal Power Control in DFIG Turbine Based Wind Farm Considering Wake Effect and Lifetime

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    Frequency Performance Assessment of Future Grids

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    Future grids security will be challenged by the increasing penetration of non-synchronous renewable energy sources (NS-RES). Studies of future grids with high penetration of NS-RES suggest that along with other issues, system frequency control will become a challenging task. Therefore, this thesis first, studies the impact of high penetration of NS-RES and different penetration levels of prosumers on the performance and frequency stability of the Australian national electricity market (NEM). By doing this, the connection between the NS-RES and the system frequency performance, as well as different penetration levels of prosumers and the system frequency performance are quantified. Second, we propose a frequency performance assessment framework based on a timeseries approach that facilitates the analysis of a large number of scenarios. This framework is utilised to assess the frequency performance of the Australian future grid by considering a large number of future scenarios and sensitivity of different parameters. By doing this, we identify a maximum non-synchronous instantaneous penetration range for the system from the frequency performance point of view. Then, to improve the frequency performance of the system with high penetration levels of NS-RES, we evaluate the contribution of different resources, such as synchronous condensers, wind farm’s synthetic inertia and a governor-like response from the de-loaded wind farms, on frequency control. The results show that the last one adds more flexibility to the system for frequency control. Finally, a coordinated operation strategy for wind farms is proposed. It is shown that by operating the wind farm in a coordinated way, we can increase both the output power and the rotational kinetic energy of the wind farm. Time-domain simulations show that the proposed operation strategies noticeably improve the wind farm’s performance in frequency control

    Wind Farm Control under Generator Faults

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    Investigation and assessment of the benefits for power systems from wind farm control

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    As wind turbines are increasingly situated in large arrays offshore, connected to power grids by a single long cable, it is necessary to consider the operation of the whole wind farm as a single plant rather than as a series of individual units. To achieve this, the development of advanced wind farm modelling software is required to test and evaluate new control strategies for wind farm operation. This thesis considers the use of Strathfarm, The University of Strathclyde’s in-house wind farm modelling software, presenting novel wind farm control algorithms which significantly reduce the fatigue of wind turbine towers and wind turbine blades. The thesis also further develops Strathfarm in two key areas, presenting improvements to the modelled wakes and also details the development of a novel power system model. The power system model can be used to show the efficacy of previously developed dispatch algorithms for wind farms to support power grids.As wind turbines are increasingly situated in large arrays offshore, connected to power grids by a single long cable, it is necessary to consider the operation of the whole wind farm as a single plant rather than as a series of individual units. To achieve this, the development of advanced wind farm modelling software is required to test and evaluate new control strategies for wind farm operation. This thesis considers the use of Strathfarm, The University of Strathclyde’s in-house wind farm modelling software, presenting novel wind farm control algorithms which significantly reduce the fatigue of wind turbine towers and wind turbine blades. The thesis also further develops Strathfarm in two key areas, presenting improvements to the modelled wakes and also details the development of a novel power system model. The power system model can be used to show the efficacy of previously developed dispatch algorithms for wind farms to support power grids

    Model predictive control strategy in waked wind farms for optimal fatigue loads

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    With the rapid growth of wind power penetration, wind farms (WFs) are required to implement frequency regulation that active power control to track a given power reference. Due to the wake interaction of the wind turbines (WTs), there is more than one solution to distributing power reference among the operating WTs, which can be exploited as an optimization problem for the second goal, such as fatigue load alleviation. In this paper, a closed-loop model predictive controller is developed that minimizes the wind farm tracking errors, the dynamical fatigue load, and and the load equalization. The controller is evaluated in a mediumfidelity model. A 64 WTs simulation case study is used to demonstrate the control performance for different penalty factor settings. The results indicated the WF can alleviate dynamical fatigue load and have no significant impact on power tracking. However, the uneven load distribution in the wind turbine system poses challenges for maintenance. By adding a trade-off between the load equalization and dynamical fatigue load, the load differences between WTs are significantly reduced, while the dynamical fatigue load slightly increases when selecting a proper penalty factor.Comment: Accepted by Electric Power Systems Researc

    Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data

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    The paper explores the deep neural learning (DNL) based predictive control approach for offshore wind farm using high fidelity large eddy simulations (LES) data. The DNL architecture is defined by combining the Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN) for feature extraction and prediction of the offshore wind farm. This hybrid CNN-LSTM model is developed based on the dynamic models of the wind farm and wind turbines as well as higher-fidelity LES data. Then, distributed and decentralized model predictive control (MPC) methods are developed based on the hybrid model for maximizing the wind farm power generation and minimizing the usage of the control commands. Extensive simulations based on a two-turbine and a nine-turbine wind farm cases demonstrate the high prediction accuracy (97% or more) of the trained CNN-LSTM models. They also show that the distributed MPC can achieve up to 38% increase in power generation at farm scale than the decentralized MPC. The computational time of the distributed MPC is around 0.7s at each time step, which is sufficiently fast as a real-time control solution to wind farm operations

    Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

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    The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

    Reactive Power Dispatch Method in Wind Farms to Improve the Lifetime of Power Converter Considering Wake Effect

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    Advanced wind farm control strategies for enhancing grid support

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    Aplicat embargament des de la data de defensa fins al maig 2020Nowadays, there is rising concern among Transmission System Operators about the declining of system inertia due to the increasing penetration of wind energy, and other renewable energy systems, and the retirements of conventional power plants. On the other hand, by properly operating wind farms, wind generation may be capable of enhancing grid stability and ensuring continued security of power supply. In this doctoral thesis, new control approaches for designing wind farm optimization-based control strategies are proposed to improve the participation of wind farms in grid support, specially in primary frequency support.Hoy en día, existe una significativa preocupación entre los Operadores de Sistemas de Transmisión sobre la cresciente penetración de le energía eólica y la tendiente eliminación de las centrales eléctricas convencionales que implica la disminución de la inercia del sistema eléctrico. Operando adecuadamente los parques eólicos, la generación eólica puede mejorar la estabilidad de la red eléctrica y garantizar la seguridad y un continuo suministro de energía. Esta tesis doctoral propone nuevas estrategias para diseñar controladores basados en optimización dinámica para parques eólicos y mejorar la participación de los parques eólicos en el soporte de la red eléctrica. En primer lugar, esta tesis doctoral presenta los enfoques clásicos para el control de parques y turbinas eólicas y cómo los conceptos de control existentes pueden ser explotados para hacer frente a los nuevos desafíos que se esperan de los parques eólicos. Esta tesis doctoral asigna un interés especial a cómo formular la función objetivo de que la reserva de potencia sea maximizada, para ayudar por el suporte de frequencia, y al mismo tiempo seguir la potencia demandada por la red. Sin embargo, el impacto de la estela de viento generada por una turbina sobre otras turbinas necesita ser minimizado para mejorar la reserva de potencia. Por lo tanto, a lo largo de esta tesis se proponen estrategias de control centralizado para parques eólicos enfocadas en distribuir óptimamente la energía entre las turbinas para que el impacto negativo de la estela en la reserva de potencia total se reduzca. Se discuten dos técnicas de control para proporcionar los objetivos de control mencionados anteriormente. Un algoritmo de control óptimo para encontrar la mejor distribución de potencia entre las turbinas en el parque mientras se resuelve un problema iterativo de programación lineal. En segundo lugar, se utiliza la técnica de control predictivo basada en modelo para resolver un problema de control multi-objetivo que también podría incluir, junto con la maximización de reserva de potencia, otros objetivos de control, tales como la minimización de las perdidas eléctricas en los cables de la red de interconexión entre turbinas y un controlador/supervisor. Además, la investigación realizada resalta la capacidad de las estrategias de control propuestas en esta tesis para proporcionar mayor reserva de potencia respecto a los conceptos comúnmente usados para distribuir la potencia total del parque eólico. La idea principal detrás del diseño de una estrategia de control de parques eólico es de encontrar una solución óptima dentro de un cálculo computacional relativamente bajo. Aunque los controladores centralizados propuestos en esta tesis reaccionan rápidamente a los cambios en la potencia de referencia enviada desde el controlador, algunos problemas pueden ocurrir cuando se consideran parques eólicos de gran escala debido a los retrasos existentes por el viento entre turbinas. Bajo estas circunstancias, la producción de energía de cada turbina está altamente acoplada con la propagación de la estela y, por ende, con las condiciones de funcionamiento de las otras turbinas. Esta tesis doctoral propone un esquema de control de parques eólicos no centralizados basado en una estrategia de partición para dividir el parque eólico en sub-conjuntos independientes de turbinas. Con la estrategia de control propuesta, el tiempo de cálculo se reduce adecuadamente en comparación con la estrategia de control centralizado mientras que el rendimiento en la distribución óptima de potencia es ligeramente afectado. El rendimiento de todas las estrategias de control propuestas en esta tesis se prueba con un simulador de parque eólico que modela el comportamiento dinámico del efecto de estela mediante el uso de un conocido y consolidado modelo dinámico de estela y, para un análisis más realista, algunas simulaciones se realizan con software avanzado basado en la técnica de Large Eddy Simulation.Postprint (published version
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